In 2008, Roytvarf and Shusterman (IEEE Trans Biomed Eng. 2008; 55 (2):407-18) reported on the development and validation of a mathematical model for dynamical tracking of cardiovascular homeostasis (including systemic blood pressure, cardiac output and systemic peripheral resistance). In that 2008 report, Roytvarf and Shusterman have also disclosed a system for measuring arterial pressure waveforms and pulse-transit time (i.e. the time required for arterial-pressure wave to travel between any two points of the arterial system), to provide input data for the mathematical model. That system used the (photo-) plethysmographic sensor on one of the fingers to measure the pressure waveforms and the ECG sensors to determine the time of occurrence of the R-wave (as a surrogate for the start time of the arterial pressure wave). The same study also demonstrated substantial variability in peripheral vascular activity (measured on one of the fingers), which complicated dynamical tracking of systemic arterial pressure patterns.
Similar disorders might have different dynamical patterns of health data, clinical manifestations and prognoses in patients with different genetic variants. For instance, cardiac arrhythmias and sudden death in Long QT type 1 are usually associated with intense physical activity (swimming) or emotions, whereas in Long QT type 3 and Brugada syndrome, arrhythmias and sudden death usually occur during sleep or at rest. The traditional electrocardiographic (ECG) analysis has identified typical ECG changes associated with these disorders; for example, a long QT-interval on the ECG is a hallmark of the Long QT syndrome. However, the characteristic dynamical patterns and their relationships to the genetic variants, mutations and genetic polymorphisms, gene and protein expression levels are largely unknown. The predictive value of such dynamical patterns associated with different genetic sub-types currently is also unknown.